Redistribution effects of water tariffs
Nguyen Bich Ngoc, Jacques Teller 13 January 2022

|
|
components
|
mean
|
sd
|
X0.
|
X25.
|
X50.
|
X75.
|
X100.
|
|
fixedpc
|
fixedpc
|
42.2477053
|
21.1582293
|
8.4256732
|
25.3063671
|
36.5688283
|
56.9366554
|
89.1224984
|
|
volpc
|
volpc
|
57.4800075
|
21.1322581
|
10.7675390
|
42.7946796
|
63.1426726
|
74.4054907
|
91.2801227
|
|
fsapc
|
fsapc
|
0.2722872
|
0.0348113
|
0.1055832
|
0.2732136
|
0.2825452
|
0.2883638
|
0.3154574
|
## Warning: Removed 150 row(s) containing missing values (geom_path).
## Warning: Removed 150 row(s) containing missing values (geom_path).
## Warning: Removed 192 row(s) containing missing values (geom_path).

## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.2122 0.7290 1.0389 1.2432 1.5183 9.0135
sum(df$TEH > 3) * 100 / nrow(df)
## [1] 3.645833
## 3.2 by income quantiles -------
### income quantiles -------
Household income quintile characteristics
|
Quintile
|
Number of households
|
Average household size
|
Min income (EUR/month)
|
Max income (EUR/month)
|
|
1
|
346
|
1.59
|
125
|
1750
|
|
2
|
346
|
2.01
|
1750
|
2250
|
|
3
|
346
|
2.38
|
2250
|
2750
|
|
4
|
345
|
2.80
|
2750
|
3750
|
|
5
|
345
|
3.35
|
3750
|
5250
|

# Correlation between water consumption and household income should use spearman?????
cor.test(df$csmptv, df$income, method = "pearson")
##
## Pearson's product-moment correlation
##
## data: df$csmptv and df$income
## t = 15.729, df = 1726, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3121384 0.3946480
## sample estimates:
## cor
## 0.354082
cor.test(df$csmptv, df$income, method = "spearman")
## Warning in cor.test.default(df$csmptv, df$income, method = "spearman"):
## Cannot compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: df$csmptv and df$income
## S = 536353649, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.3763062
# Correlation between water consumption and income per equivalent adult should use spearman?????
cor.test(df$csmptv, df$inceqa, method = "pearson")
##
## Pearson's product-moment correlation
##
## data: df$csmptv and df$inceqa
## t = 1.4269, df = 1726, p-value = 0.1538
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.01285165 0.08134826
## sample estimates:
## cor
## 0.03432454
cor.test(df$csmptv, df$inceqa, method = "spearman")
## Warning in cor.test.default(df$csmptv, df$inceqa, method = "spearman"):
## Cannot compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: df$csmptv and df$inceqa
## S = 803896839, p-value = 0.006707
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.06519613

|
incqnt
|
fixedpc_fn1
|
volpc_fn1
|
fsapc_fn1
|
fixedpc_fn2
|
volpc_fn2
|
fsapc_fn2
|
fixed
|
vol
|
fsa
|
|
1
|
54.70
|
45.04
|
0.25
|
21.83
|
21.79
|
0.048
|
54.70±21.83
|
45.04±21.79
|
0.25±0.048
|
|
2
|
46.74
|
52.99
|
0.27
|
20.81
|
20.78
|
0.035
|
46.74±20.81
|
52.99±20.78
|
0.27±0.035
|
|
3
|
39.87
|
59.85
|
0.28
|
19.26
|
19.24
|
0.029
|
39.87±19.26
|
59.85±19.24
|
0.28±0.029
|
|
4
|
36.94
|
62.78
|
0.28
|
18.04
|
18.02
|
0.022
|
36.94±18.04
|
62.78±18.02
|
0.28±0.022
|
|
5
|
32.95
|
66.77
|
0.28
|
18.47
|
18.44
|
0.028
|
32.95±18.47
|
66.77±18.44
|
0.28±0.028
|
|
incqnt
|
tehprop
|
|
1
|
12.7167630
|
|
2
|
3.7572254
|
|
3
|
1.4450867
|
|
4
|
0.2898551
|
|
5
|
0.0000000
|
## Warning: Graphs cannot be horizontally aligned unless the axis parameter
## is set. Placing graphs unaligned.

## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 4.200 4.595 4.690 5.000 4.850 12.549
summary(df$avrprc[df$inccat == "precarious"])
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 4.222 4.605 4.734 5.152 4.915 10.592 1
summary(df$subs[df$inccat == "precarious"])
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -68.1035 -4.7756 0.5658 -4.0094 8.3744 27.4910 1
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.158 4.458 4.458 3.982 4.458 4.658
summary(df$mgnprc[df$inccat == "precarious"])
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.158 4.153 4.458 3.754 4.458 4.658 1
## 3.5. changing fixed -----
### new cvd ------
|
CVD_SWDE
|
CVD_CILE
|
CVD_inBW
|
CVA
|
scenario
|
fixed
|
rwtt
|
mgpr_bl1
|
mgpr_bl2
|
|
2.4480
|
2.6366
|
2.1600
|
1.745
|
As in 2014
|
101.438
|
0
|
1.2272
|
4.1994
|
|
4.2744
|
4.5442
|
3.6839
|
1.745
|
1
|
0.000
|
0
|
2.1344
|
6.0138
|
|
3.3730
|
3.6365
|
2.8864
|
1.745
|
2
|
50.000
|
0
|
1.6875
|
5.1200
|
|
2.4716
|
2.7289
|
2.0890
|
1.745
|
3
|
100.000
|
0
|
1.2406
|
4.2262
|
|
1.5702
|
1.8212
|
1.2916
|
1.745
|
4
|
150.000
|
0
|
0.7937
|
3.3324
|
|
0.6688
|
0.9135
|
0.4942
|
1.745
|
5
|
200.000
|
0
|
0.3468
|
2.4386
|

|
CVD_SWDE
|
CVD_CILE
|
CVD_inBW
|
CVA
|
scenario
|
fixed
|
rwtt
|
mgpr_bl1
|
mgpr_bl2
|
|
2.4480
|
2.6366
|
2.1600
|
1.745
|
6
|
101.4380
|
0
|
1.2272
|
4.1994
|
|
2.1517
|
2.4683
|
1.8852
|
1.745
|
7
|
95.9578
|
50
|
1.0902
|
3.9254
|
|
1.8554
|
2.3000
|
1.6104
|
1.745
|
8
|
90.4777
|
100
|
0.9532
|
3.6514
|
|
1.5591
|
2.1318
|
1.3356
|
1.745
|
9
|
84.9975
|
150
|
0.8162
|
3.3774
|
|
1.2628
|
1.9635
|
1.0608
|
1.745
|
10
|
79.5174
|
200
|
0.6792
|
3.1034
|

|
bl1_SWDE
|
bl1_CILE
|
bl1_inBW
|
fixed
|
revincr
|
mgpr_bl1
|
mgpr_bl2
|
|
4.427231
|
4.635599
|
4.069370
|
0
|
0.0
|
4.431018
|
4.431018
|
|
3.710676
|
3.914732
|
3.423071
|
50
|
0.0
|
3.719586
|
3.719586
|
|
2.994121
|
3.193866
|
2.776773
|
100
|
0.0
|
3.008154
|
3.008154
|
|
4.871204
|
5.100408
|
4.477557
|
0
|
0.1
|
4.875369
|
4.875369
|
|
4.154649
|
4.379542
|
3.831259
|
50
|
0.1
|
4.163938
|
4.163938
|
|
3.438094
|
3.658676
|
3.184960
|
100
|
0.1
|
3.452506
|
3.452506
|
|
5.315177
|
5.565218
|
4.885744
|
0
|
0.2
|
5.319721
|
5.319721
|
|
4.598622
|
4.844352
|
4.239446
|
50
|
0.2
|
4.608289
|
4.608289
|
|
3.882067
|
4.123486
|
3.593147
|
100
|
0.2
|
3.896857
|
3.896857
|
|
bl1_SWDE
|
bl1_CILE
|
bl1_inBW
|
fixed
|
revincr
|
mgpr_bl1
|
mgpr_bl2
|
|
1.789001
|
1.876093
|
1.594286
|
0
|
0.0
|
1.786848
|
6.253969
|
|
1.499448
|
1.584348
|
1.341081
|
50
|
0.0
|
1.499952
|
5.249832
|
|
1.209895
|
1.292603
|
1.087876
|
100
|
0.0
|
1.213056
|
4.245695
|
|
1.968406
|
2.064208
|
1.754204
|
0
|
0.1
|
1.966037
|
6.881130
|
|
1.678853
|
1.772463
|
1.500999
|
50
|
0.1
|
1.679141
|
5.876993
|
|
1.389300
|
1.480718
|
1.247794
|
100
|
0.1
|
1.392245
|
4.872856
|
|
2.147811
|
2.252323
|
1.914122
|
0
|
0.2
|
2.145226
|
7.508291
|
|
1.858259
|
1.960578
|
1.660917
|
50
|
0.2
|
1.858330
|
6.504154
|
|
1.568706
|
1.668834
|
1.407712
|
100
|
0.2
|
1.571433
|
5.500017
|
|
bl1_SWDE
|
bl1_CILE
|
bl1_inBW
|
fixed
|
revincr
|
mgpr_bl1
|
mgpr_bl2
|
|
1.816621
|
1.870233
|
1.608886
|
0
|
0.0
|
1.808024
|
6.328085
|
|
1.522598
|
1.579399
|
1.353362
|
50
|
0.0
|
1.517698
|
5.311944
|
|
1.228575
|
1.288566
|
1.097838
|
100
|
0.0
|
1.227372
|
4.295803
|
|
1.998797
|
2.057760
|
1.770269
|
0
|
0.1
|
1.989337
|
6.962678
|
|
1.704773
|
1.766927
|
1.514745
|
50
|
0.1
|
1.699011
|
5.946537
|
|
1.410750
|
1.476093
|
1.259221
|
100
|
0.1
|
1.408685
|
4.930397
|
|
2.180972
|
2.245288
|
1.931651
|
0
|
0.2
|
2.170649
|
7.597272
|
|
1.886948
|
1.954454
|
1.676128
|
50
|
0.2
|
1.880323
|
6.581131
|
|
1.592925
|
1.663621
|
1.420604
|
100
|
0.2
|
1.589997
|
5.564990
|
